Abstract
Whenever someone makes or receives a call on a mobile telephone, a Call Detail Record (CDR) is automatically generated by the operator for billing purposes. CDRs have a wide range of applications beyond billing, from social science to data-driven development. Recently, CDRs have been increasingly used to study human mobility, whose understanding is crucial e.g. for planning efficient transportation infrastructure. A major difficulty in analyzing human mobility using CDR data is that the location of a cell phone user is not recorded continuously but typically only when a call is initiated or a text message is sent. In this paper we address this problem, and develop a method for estimating travel times between cities based on CDRs that relies not on individual trajectories of people, but their collective statistical properties. We apply our method to data from Senegal, released by Sonatel and Orange for the 2014 Data for Development Challenge. We turn CDR mobility traces to estimates on travel times between Senegalese cities, filling an existing gap in knowledge. Moreover, the proposed method is shown to be highly valuable for monitoring travel conditions and their changes in near real-time, as demonstrated by measuring the decrease in travel times due to the opening of the Dakar-Diamniadio highway. Overall, our results indicate that it is possible to extract reliable de facto information on typical travel times that is useful for a variety of audiences ranging from casual travelers to transport infrastructure planners.
Highlights
1 Introduction Mobile phones are ubiquitous, widely available and used all over the world. They have proven to be an invaluable source of high-quality data for studying different aspects of human societies [ – ], especially for development purposes [, ]. Such studies typically use Call Detail Record (CDR) data that are collected by telecommunication operators for billing purposes and come with no extra cost or overhead
There is a need for methods which ( ) are inexpensive and are not resource or labor-intensive ( ) do not depend on complicated infrastructure or hardware ( ) provide accurate estimates of travel times experienced by users. We show that this can be achieved with the help of CDR data already stored for billing purposes, without the need for implementing more detailed hand-over or triangulation data analysis pipelines
We have developed a method for automated extraction of typical travel times between cities from CDR data
Summary
Widely available and used all over the world. They have proven to be an invaluable source of high-quality data for studying different aspects of human societies [ – ], especially for development purposes [ , ]. The second is based on signaling strengths and delays between a mobile phone and nearby cell towers [ , ] When done periodically, this results in GPS-like coordinate trajectories which can be further refined into travel time distributions between origin-destination (OD) pairs [ ]. When multiple mobility trajectories are pooled and analyzed as a whole, it turns out it is possible to produce reliable travel time estimates To this end, we have developed a method for automated extraction of typical travel times between cities from CDR data. The method aims at providing an overall view on travel times between cities and it enables monitoring of travel times and conditions in the long term It has been especially designed for developing countries where reliable information on travel times and transport infrastructure is limited or not available at all. To demonstrate that the method is capable of monitoring changes in travel condition in near-real-time, we estimate how much the opening of the Dakar-Diamniadio highway dropped the typical travel times between the capital Dakar and the nearby city of Pout
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